Addressing Post-Truth in the Classroom: Towards a Critical Pedagogy
Bibliographic record
Abstract
Post-truth strategies are characterized by the manipulation of facts and personal assertions of the truth for political gain. By seeding polarization, skepticism, and mistrust, post-truth presents challenges to teaching and learning within academic settings. In this paper, we explore how post-truth is articulated in higher education literature using a critical pedagogical lens. We suggest that pedagogical scholarship needs to expand its scope beyond a focus on the media antics of individual politicians in order to interrogate the reliance on dominant framings that simply define “post-truth” as circumstances where personal beliefs take precedence over established facts. We argue that the current framing of post-truth shapes the educational response to this issue, which focuses on helping students discern correct from incorrect information, as opposed to teaching students how power and knowledge are intertwined in post-truth and ways to understand and address the subsequent and potentially harmful power relations. Since post-truth strategies are enacted to restrict thoughtful reflection on dominant relations of power, we propose a critical pedagogical framework to problematize the notion of objective truth, account for the politics of exclusion, examine power relations, and contest post-truth strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".